A neutrosophic-entropy based clustering algorithm (NEBCA) with HSV color system: A special application in segmentation of Parkinson's disease (PD) MR images

Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm

Accurate road extraction and recognition of roadside fruit in complex orchard environments are essential prerequisites for robotic fruit picking and walking behavioral decisions. In this study, a novel algorithm was proposed for unstructured road extraction and roadside fruit synchronous recognition, with wine grapes and nonstructural orchards as research objects. Initially, a preprocessing method tailored to field orchards was proposed to reduce the interference of adverse factors in the operating environment. The preprocessing method contained 4 parts: interception of regions of interest, bilateral filter, logarithmic space transformation and image enhancement based on the MSRCR algorithm. Subsequently, the analysis of the enhanced image enabled the optimization of the gray factor, and a road region extraction method based on dual-space fusion was proposed by color channel enhancement and gray factor optimization. Furthermore, the YOLO model suitable for grape cluster recognition in the wild environment was selected, and its parameters were optimized to enhance the recognition performance of the model for randomly distributed grapes. Finally, a fusion recognition framework was innovatively established, wherein the road extraction result was taken as input, and the optimized parameter YOLO model was utilized to identify roadside fruits, thus realizing synchronous road extraction and roadside fruit detection. Experimental results demonstrated that the proposed method based on the pretreatment could reduce the impact of interfering factors in complex orchard environments and enhance the quality of road extraction. Using the optimized YOLOv7 model, the precision, recall, mAP, and F1-score for roadside fruit cluster detection were 88.9%, 89.7%, 93.4%, and 89.3%, respectively, all of which were higher than those of the YOLOv5 model and were more suitable for roadside grape recognition. Compared to the identification results obtained by the grape detection algorithm alone, the proposed synchronous algorithm increased the number of fruit identifications by 23.84% and the detection speed by 14.33%. This research enhanced the perception ability of robots and provided a solid support for behavioral decision systems.

Environmental application of a cost-effective smartphone-based method for COD analysis: Applicability in the electrochemical treatment of real wastewater

This study aims to develop a cheap method for the evaluation of quality of water or the assessment of the treatment of water by chemical oxygen demand (COD) measurements throughout the use of the HSV color model in digital devices. A free application installed on a smartphone was used for analyzing the images in which the colors were acquired before to be quantified. The proposed method was also validated by the standard and spectrophotometric methods, demonstrating that no significant statistical differences were attained (average accuracy of 97 %). With these results, the utilization of this smartphone-based method for COD analysis was used/evaluated, for first time, by treating electrochemically a real water matrix with substantial organic and salts content using BDD and Pt/Ti anodes. Aiming to understand the performance of both anodes, bulk experiments were performed under real pH by applying current densities (j) of 15, 30, and 60 mA cm^-2. COD abatement results (which were achieved with this novel smart water security solution) clearly showed that different organic matter removal efficiencies were achieved, depending on the electrocatalytic material used as well as the applied current density (42 %, 45 %, and 85 % for Ti/Pt while 93 %, 97 % and total degradation for BDD by applying 15, 30, and 60 mA cm^-2, respectively). However, when the persulfate-mediated oxidation approach was used, with the addition of 2 or 4 g Na₂SO₄ L^-1, COD removal efficiencies were enhanced, obtaining total degradation with 4 g Na₂SO₄ L^-1 and by applying 15 mA cm^-2. Finally, this smartphone imaging-based method provides a simple and rapid method for the evaluation of COD during the use of electrochemical remediation technology, developing and decentralizing analytics technologies for smart water solutions which play a key role in achieving the Sustainable Development Goal 6 (SDG6).

SPCM: Efficient semi-possibilistic c-means clustering algorithm

The required division and exponentiation operations needed per iteration for the possibilistic c-means (PCM) clustering algorithm complicate its implementation, especially on homomorphically-encrypted data. This paper presents a novel efficient soft clustering algorithm based on the possibilistic paradigm, termed SPCM. It aims at easing future applications of PCM to encrypted data. It reduces the required exponentiation and division operations at each iteration by restricting the membership values to an ordered set of discrete values in [0,1], resulting in a better performance in terms of runtime and several other performance indices. At each iteration, distances to the new clusters’ centers are determined, then the distances are compared to the initially computed and dynamically updated range of values, that divide the entire range of distances associated with each cluster center into intervals (bins), to assign appropriate soft memberships to objects. The required number of comparisons is O(log the number of discretization levels). Thus, the computation of centers and memberships is greatly simplified during execution. Also, the use of discrete values for memberships allows soft modification (increment or decrement) of the soft memberships of identified outliers and core objects instead of rough modification (setting to zero or one) in related algorithms. Experimental results on synthetic and standard test data sets verified the efficiency and effectiveness of the proposed algorithm. The average percent of the achieved reduction in runtime is 35% and the average percent of the achieved increase in v-measure, adjusted mutual information, and adjusted rand index is 6% on five datasets compared to PCM. The larger the dataset, the higher the reduction in runtime. Also, SPCM achieved a comparable performance with less computational complexity compared to variants of related algorithms.

Deep unsupervised endoscopic image enhancement based on multi-image fusion

BACKGROUND AND OBJECTIVE

A deep unsupervised endoscopic image enhancement method is proposed based on multi-image fusion to achieve high quality endoscope images from poorly illuminated, low contrast and color deviated images through an unsupervised mapping and deep learning network without the need for ground truth.

METHODS

Firstly, three image enhancement methods are used to process original endoscopic images to obtain three derived images, which are then transformed into HSI color space. Secondly, a deep unsupervised multi-image fusion network (DerivedFuse) is proposed to extract and fuse features of the derived images accurately by utilizing a new no-reference quality metric as loss function. I-channel images of the three derived images are inputted into the DerivedFuse network to enhance the intensity component of the original image. Finally, a saturation adjustment function is proposed to adaptive adjusting the saturation component of HSI color space to enrich the color information of the original input image.

RESULTS

Three evaluation metrics: Entropy, Contrast Improvement Index (CII) and Average Gradient (AG) are used to evaluate the performance of the proposed method. The results are compared with that of fourteen state-of-the-art algorithms. Experiments on endoscopic image enhancement show that the Entropy value of our method is 3.27% higher than the optimal entropy value of comparison algorithms. The CII of our proposed method is 6.19% higher than that of comparison algorithms. The AG of our method is 7.83% higher than the optimal AG of comparison algorithms.

CONCLUSIONS

The proposed deep unsupervised multi-image fusion method can obtain image information details, enhance endoscopic images with high contrast, rich and natural color information, visual and image quality. Sixteen doctors and medical students have given their assessments on the proposed method for assisting clinical diagnoses.

Analysis of fMRI Signals from Working Memory Tasks and Resting-State of Brain: Neutrosophic-Entropy-Based Clustering Algorithm

This study applies a neutrosophic-entropy-based clustering algorithm (NEBCA) to analyze the fMRI signals. We consider the data obtained from four different working memory tasks and the brain's resting state for the experimental purpose. Three non-overlapping clusters of data related to temporal brain activity are determined and statistically analyzed. Moreover, we used the Uniform Manifold Approximation and Projection (UMAP) method to reduce system dimensionality and present the effectiveness of NEBCA. The results show that using NEBCA, we are able to distinguish between different working memory tasks and resting-state and identify subtle differences in the related activity of brain regions. By analyzing the statistical properties of the entropy inside the clusters, the various regions of interest (ROIs), according to Automated Anatomical Labeling (AAL) atlas crucial for clustering procedure, are determined. The inferior occipital gyrus is established as an important brain region in distinguishing the resting state from the tasks. Moreover, the inferior occipital gyrus and superior parietal lobule are identified as necessary to correct the data discrimination related to the different memory tasks. We verified the statistical significance of the results through the two-sample t-test and analysis of surrogates performed by randomization of the cluster elements. The presented methodology is also appropriate to determine the influence of time of day on brain activity patterns. The differences between working memory tasks and resting-state in the morning are related to a lower index of small-worldness and sleep inertia in the first hours after waking. We also compared the performance of NEBCA to two existing algorithms, KMCA and FKMCA. We showed the advantage of the NEBCA over these algorithms that could not effectively accumulate fMRI signals with higher variability.

GNN-DBSCAN: A new density-based algorithm using grid and the nearest neighbor

DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts. Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN.

A quantum-clustering optimization method for COVID-19 CT scan image segmentation

The World Health Organization (WHO) has declared Coronavirus Disease 2019 (COVID-19) as one of the highly contagious diseases and considered this epidemic as a global health emergency. Therefore, medical professionals urgently need an early diagnosis method for this new type of disease as soon as possible. In this research work, a new early screening method for the investigation of COVID-19 pneumonia using chest CT scan images has been introduced. For this purpose, a new image segmentation method based on K-means clustering algorithm (KMC) and novel fast forward quantum optimization algorithm (FFQOA) is proposed. The proposed method, called FFQOAK (FFQOA+KMC), initiates by clustering gray level values with the KMC algorithm and generating an optimal segmented image with the FFQOA. The main objective of the proposed FFQOAK is to segment the chest CT scan images so that infected regions can be accurately detected. The proposed method is verified and validated with different chest CT scan images of COVID-19 patients. The segmented images obtained using FFQOAK method are compared with various benchmark image segmentation methods. The proposed method achieves mean squared error, peak signal-to-noise ratio, Jaccard similarity coefficient and correlation coefficient of 712.30, 19.61, 0.90 and 0.91 in case of four experimental sets, namely Experimental_Set_1, Experimental_Set_2, Experimental_Set_3 and Experimental_Set_4, respectively. These four performance evaluation metrics show the effectiveness of FFQOAK method over these existing methods.

Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19

Coronavirus Disease 2019 (COVID-19) has been considered one of the most critical diseases of the 21st century. Only early detection can aid in the prevention of personal transmission of the disease. Recent scientific research reports indicate that computed tomography (CT) images of COVID-19 patients exhibit acute infections and lung abnormalities. However, analyzing these CT scan images is very difficult because of the presence of noise and low-resolution. Therefore, this study suggests the development of a new early detection method to detect abnormalities in chest CT scan images of COVID-19 patients. By this motivation, a novel image clustering algorithm, called ambiguous D-means fusion clustering algorithm (ADMFCA), is introduced in this study. This algorithm is based on the newly proposed ambiguous set theory and associated concepts. The ambiguous set is used in the proposed technique to characterize the ambiguity associated with grayscale values of pixels as true, false, true-ambiguous and false-ambiguous. The proposed algorithm performs the clustering operation on the CT scan images based on the entropies of different grayscale values. Finally, a final outcome image is obtained from the clustered images by image fusion operation. The experiment is carried out on 40 different CT scan images of COVID-19 patients. The clustered images obtained by the proposed algorithm are compared to five well-known clustering methods. The comparative study based on statistical metrics shows that the proposed ADMFCA is more efficient than the five existing clustering methods.